scRNA-seq

Overview¶

0. Google
1. Hematopoiesis
2. CellRanger
3. Scanpy and AnnData
4. Quality control, Normalization
5. Dimensionality reduction with PCA
6. Cell-cell distances
7. Unsupervised clustering with Leiden

Google Colab¶

• For a simple intro see https://compscbio.github.io/cscb2026/notebooks/colab.html
• Cloud compute resource
• To sign up for free educational user account: https://colab.research.google.com/signup
• To start using Colab directly: https://colab.research.google.com/#
• You can launch a copy of the notebook that you are feasting your eyes upon now in Colab by selecting File -> Open Notebook -> GitHub, then copy and paste this URL into the search field: https://github.com/compscbio/cscb2026/blob/gh-pages/notebooks/scBasics.ipynb

Background on the data¶

Let's start with some background + motivation for the data set that we are going to analyze: hematopoietic cells from peripheral blood.

Hematopoiesis¶

Traditional model of hematopoiesis. Image from Slack 2017

These cells are produced in the bone marrow and then move into the vascular so that they can move about the body and perform their functions (transport oxygen, clot, do adaptive and innate immune things).

Peripheral blood mononuclear cells (PBMCs)¶

It is possible to enrich for what are called PBMCs that have been taken from from a donor's vein, and then performing density gradient centrifugation. This process enriches for cells that have a single, round nucleus, and it excludes other stuff. PBMCs typically contain:

... several classes of immune cells, including T cells (~70%), B cells (~15%), monocytes (~5%), dendritic cells (~1%) and natural killer (NK) cells (~10%)
-- Sen et al 2019

and

... The CD3+ lymphocytes are composed of CD4+ and CD8+ T cells, roughly in a 2:1 ratio.
-- Kleiveland, 2015

Some questions we can explore¶

• What does scRNAseq estimate as the relative cell type proportions?
• Are there other genes that are better at distinguishing between these populations than marker genes listed below?
• How many cells do you need to reliably detect a sub-population?

Data¶

• 10X Genomics scRNA-seq on human PBMCs from a healthy donor
• About 10,000 cells
• http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz

CellRanger¶

CellRanger is the program that converts sequencing reads into gene expression counts.

CellRanger flow chart from 10X genomics

Here is a more detailed glimpse of what is happening here:

CellRanger flow chart 2 from 10X genomics

• Trims reads
• Splicing aware alignment to reference genome
• deals wth reads mapping to > 1 locus

CellRanger mapping from 10X genomics

UMI counting¶

• group reads with same cell bc, umi, gene
• correct umis (one base off)
• cell bc, umi, diff gene -> only keep gene with most support
• discard both if tied
• all reads with same cell bc, umi, gene are counted as one UMI
• number of reads contributing to that UMI are stored in molecule info file (useful later)

Setting up our Colab environment¶

Note that lines starting with ! send the remaining line to the shell

In [1]:

!pip install scanpy python-igraph scipy umap-learn leidenalg

!pip install scanpy python-igraph scipy umap-learn leidenalg

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Fetch the data¶

In [2]:

!wget https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_10k_v3/pbmc_10k_v3_filtered_feature_bc_matrix.h5

!wget https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_10k_v3/pbmc_10k_v3_filtered_feature_bc_matrix.h5

--2026-01-27 10:40:49--  https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_10k_v3/pbmc_10k_v3_filtered_feature_bc_matrix.h5
Resolving cf.10xgenomics.com (cf.10xgenomics.com)... 104.18.1.173, 104.18.0.173
Connecting to cf.10xgenomics.com (cf.10xgenomics.com)|104.18.1.173|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 37491093 (36M) [binary/octet-stream]
Saving to: ‘pbmc_10k_v3_filtered_feature_bc_matrix.h5’

pbmc_10k_v3_filtere 100%[===================>]  35.75M  36.9MB/s    in 1.0s    

2026-01-27 10:40:51 (36.9 MB/s) - ‘pbmc_10k_v3_filtered_feature_bc_matrix.h5’ saved [37491093/37491093]

Set up our Python session¶

In [3]:

import scanpy as sc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.rcParams['figure.dpi'] = 300
sc.logging.print_header()

import scanpy as sc import numpy as np import pandas as pd import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore') plt.rcParams['figure.dpi'] = 300 sc.logging.print_header()

scanpy==1.10.4 anndata==0.11.3 umap==0.5.7 numpy==1.26.4 scipy==1.15.1 pandas==2.2.3 scikit-learn==1.6.1 statsmodels==0.14.4 igraph==0.11.8 pynndescent==0.5.13

Load Sample 1¶

In [4]:

ad10f = sc.read_10x_h5("pbmc_10k_v3_filtered_feature_bc_matrix.h5")

ad10f = sc.read_10x_h5("pbmc_10k_v3_filtered_feature_bc_matrix.h5")

Getting information about functions¶

You can get info about functions using

help(functionName)

or

?functionName

In [5]:

?sc.read_10x_h5

?sc.read_10x_h5

Signature:
sc.read_10x_h5(
    filename: 'Path | str',
    *,
    genome: 'str | None' = None,
    gex_only: 'bool' = True,
    backup_url: 'str | None' = None,
) -> 'AnnData'
Docstring:
Read 10x-Genomics-formatted hdf5 file.

Parameters
----------
filename : 'Path | str'
    Path to a 10x hdf5 file.
genome : 'str | None', optional (default: None)
    Filter expression to genes within this genome. For legacy 10x h5
    files, this must be provided if the data contains more than one genome.
gex_only : 'bool', optional (default: True)
    Only keep 'Gene Expression' data and ignore other feature types,
    e.g. 'Antibody Capture', 'CRISPR Guide Capture', or 'Custom'
backup_url : 'str | None', optional (default: None)
    Retrieve the file from an URL if not present on disk.

Returns
-------
Annotated data matrix, where observations/cells are named by their
barcode and variables/genes by gene name. Stores the following information:

:attr:`~anndata.AnnData.X`
    The data matrix is stored
:attr:`~anndata.AnnData.obs_names`
    Cell names
:attr:`~anndata.AnnData.var_names`
    Gene names for a feature barcode matrix, probe names for a probe bc matrix
:attr:`~anndata.AnnData.var`\ `['gene_ids']`
    Gene IDs
:attr:`~anndata.AnnData.var`\ `['feature_types']`
    Feature types
:attr:`~anndata.AnnData.obs`\ `[filtered_barcodes]`
    filtered barcodes if present in the matrix
:attr:`~anndata.AnnData.var`
    Any additional metadata present in /matrix/features is read in.
File:      /opt/homebrew/Caskroom/miniforge/base/envs/mkdocs/lib/python3.12/site-packages/scanpy/readwrite.py
Type:      function

Package documentation¶

Often, the best source of help is the package's documentation.

Scanpy's documentation

AnnData¶

In [7]:

ad10f

ad10f

Out[7]:

AnnData object with n_obs × n_vars = 11769 × 33538
    var: 'gene_ids', 'feature_types', 'genome'

In [8]:

type(ad10f)

type(ad10f)

Out[8]:

anndata._core.anndata.AnnData

In [9]:

ad10f.var_names_make_unique()

ad10f.var_names_make_unique()

In [10]:

print(ad10f)

print(ad10f)

AnnData object with n_obs × n_vars = 11769 × 33538
    var: 'gene_ids', 'feature_types', 'genome'

In [11]:

ad10f.shape

ad10f.shape

Out[11]:

(11769, 33538)

In [8]:

type(ad10f)

type(ad10f)

Out[8]:

anndata._core.anndata.AnnData

AnnData¶

AnnData is a Python package that defines a data structure designed to efficiently store large data sets like scRNA-seq.

AnnData schema. AnnData documentation. See Resouces below for more

Metadata are stored in:

• .obs for cells
• .var for genes
• .uns for unstructured metadata

Let's look at .obs first

In [12]:

ad10f.obs

ad10f.obs

Out[12]:

AAACCCAAGCGCCCAT-1
AAACCCAAGGTTCCGC-1
AAACCCACAGAGTTGG-1
AAACCCACAGGTATGG-1
AAACCCACATAGTCAC-1
...
TTTGTTGGTGTCATGT-1
TTTGTTGGTTTGAACC-1
TTTGTTGTCCAAGCCG-1
TTTGTTGTCTTACTGT-1
TTTGTTGTCTTCTAAC-1

11769 rows × 0 columns

We have not added any information so all that this slot holds now are cell barcodes. What about .var?

ad10f.var

In [ ]:

Here we can see that each row has a gene symbol, a gene ID from Ensembl, and some additional information about the gene and genome build.
.var and .obs are Pandas data frames. To list all gene annotataions in .var:

Here we can see that each row has a gene symbol, a gene ID from Ensembl, and some additional information about the gene and genome build. .var and .obs are Pandas data frames. To list all gene annotataions in .var:

In [14]:

ad10f.var.keys()

ad10f.var.keys()

Out[14]:

Index(['gene_ids', 'feature_types', 'genome'], dtype='object')

You might be wondering where are the gene counts? They are stored in .X:

In [15]:

ad10f.X

ad10f.X

Out[15]:

<Compressed Sparse Row sparse matrix of dtype 'float32'
	with 24825783 stored elements and shape (11769, 33538)>

Sparse data structures store only non-zero entries and their index to conserve RAM. Let's look at .X:

In [18]:

# look at some non-zero values
print(ad10f.X.data)
# what are their indices / positions?
print(ad10f.X.indices)
# compute the fraction of non-zero entries
print(ad10f.X.nnz / np.prod(ad10f.X.shape))

# look at some non-zero values print(ad10f.X.data) # what are their indices / positions? print(ad10f.X.indices) # compute the fraction of non-zero entries print(ad10f.X.nnz / np.prod(ad10f.X.shape))

[1. 4. 2. ... 1. 1. 1.]
[33508 33505 33503 ...   166   154    95]
0.0628964641931576

In [20]:

ad10f.X.nnz

ad10f.X.nnz

Type:        property
String form: <property object at 0x13288bab0>
Docstring:  
Number of stored values, including explicit zeros.

See also
--------
count_nonzero : Number of non-zero entries

Lots of times it is useful to have different versions or treansformations of the expression data. For example, later on in this tutorial we will normalize the expression data. To avoid overwriting the raw counts data, we can store it a different 'layer':

In [23]:

ad10f.layers['counts'] = ad10f.X.copy()
ad10f.layers

ad10f.layers['counts'] = ad10f.X.copy() ad10f.layers

Out[23]:

Layers with keys: counts

.X is the default slot that scanpy functions will use but most functions have a layer parameter that allow you to specify another one if needed.

Selecting genes and cells¶

obs_names and var_names are indicies into .obs and .var and .X.

In [26]:

ad10f.obs_names

ad10f.obs_names

Out[26]:

Index(['AAACCCAAGCGCCCAT-1', 'AAACCCAAGGTTCCGC-1', 'AAACCCACAGAGTTGG-1',
       'AAACCCACAGGTATGG-1', 'AAACCCACATAGTCAC-1', 'AAACCCACATCCAATG-1',
       'AAACCCAGTGGCTACC-1', 'AAACCCATCCCGAGAC-1', 'AAACCCATCTGGCCGA-1',
       'AAACCCATCTGTTCAT-1',
       ...
       'TTTGTTGGTAGCACGA-1', 'TTTGTTGGTATCAGCT-1', 'TTTGTTGGTCGGTGAA-1',
       'TTTGTTGGTCTGTAAC-1', 'TTTGTTGGTGCGTCGT-1', 'TTTGTTGGTGTCATGT-1',
       'TTTGTTGGTTTGAACC-1', 'TTTGTTGTCCAAGCCG-1', 'TTTGTTGTCTTACTGT-1',
       'TTTGTTGTCTTCTAAC-1'],
      dtype='object', length=11769)

In [27]:

ad10f.var_names

ad10f.var_names

Out[27]:

Index(['MIR1302-2HG', 'FAM138A', 'OR4F5', 'AL627309.1', 'AL627309.3',
       'AL627309.2', 'AL627309.4', 'AL732372.1', 'OR4F29', 'AC114498.1',
       ...
       'AC007325.2', 'BX072566.1', 'AL354822.1', 'AC023491.2', 'AC004556.1',
       'AC233755.2', 'AC233755.1', 'AC240274.1', 'AC213203.1', 'FAM231C'],
      dtype='object', length=33538)

To subset annData, just specfiy the cells and genes:

In [29]:

genes_monocyte = ["CD14", "CD68", "LYZ"]
adSubset = ad10f[:10, genes_monocyte]
adSubset.shape

genes_monocyte = ["CD14", "CD68", "LYZ"] adSubset = ad10f[:10, genes_monocyte] adSubset.shape

Out[29]:

(10, 3)

Select cells that express CD14, CD68, and LYZ:

In [36]:

gene_mask = ad10f[:,genes_monocyte].X > 0
gene_mask = gene_mask.toarray()
passing_cells = np.all(gene_mask, axis=1) # could use np.any to allow a cell expressing any of these genes to pass
adMono = ad10f[passing_cells]
adMono.shape

gene_mask = ad10f[:,genes_monocyte].X > 0 gene_mask = gene_mask.toarray() passing_cells = np.all(gene_mask, axis=1) # could use np.any to allow a cell expressing any of these genes to pass adMono = ad10f[passing_cells] adMono.shape

Out[36]:

(3262, 33538)

Other slots in adata:

• .obsm and .varm: multidimensional metadata
• .obsp and .varp: metadata on paired cells and paired genes

We will revisit these later.

Views
Views are pointers to memory where original anndata is stored. They are not copies of the variable.

Let's add some cell metadata

In [37]:

ad10f.obs['sample_name'] = "sample_1"
print(ad10f.obs.columns)

ad10f.obs['sample_name'] = "sample_1" print(ad10f.obs.columns)

Index(['sample_name'], dtype='object')

Quality Control (QC)¶

Broadly speaking there are two things that we apply quality control to:

1. Cells: We want to find putative doublets and low quality cell barcodes and ehold them out of downstream analysis (or at least tag them).
2. Genes: We also can remove genes that are not detected in the vast majority of cell

The function sc.pp.calculate_qc_metrics() will calculate a set of metrics on adata and places the results in .obs and .var. The results can then be used to filter the adata. pp.calculate_qc_metrics() can also take inputs to specify other, custom metrics to compute. Below, we use it to compute the fraction of all UMIs in each cell that are mitochondrially encoded or that encode for ribosomal genes.

In [38]:

ad10f.var['mt'] = ad10f.var_names.str.startswith('MT-')
ribo_prefix = ("RPS","RPL")
ad10f.var['ribo'] = ad10f.var_names.str.startswith(ribo_prefix)
sc.pp.calculate_qc_metrics(ad10f, qc_vars=['mt','ribo'], percent_top=None, log1p=False, inplace=True)

ad10f.var['mt'] = ad10f.var_names.str.startswith('MT-') ribo_prefix = ("RPS","RPL") ad10f.var['ribo'] = ad10f.var_names.str.startswith(ribo_prefix) sc.pp.calculate_qc_metrics(ad10f, qc_vars=['mt','ribo'], percent_top=None, log1p=False, inplace=True)

Visualize¶

Take a look some typical QC values across these data

In [39]:

adClean = ad10f.copy()

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10,4), gridspec_kw={'wspace':0.25}, constrained_layout=True)
ax1_dict = sc.pl.scatter(adClean, x='total_counts', y='pct_counts_mt', ax=ax1, show=False)
ax2_dict = sc.pl.scatter(adClean, x='total_counts', y='n_genes_by_counts',ax=ax2, show=False)
ax3_dict = sc.pl.scatter(adClean, x='pct_counts_ribo', y='n_genes_by_counts',ax=ax3, show=False)
plt.show()

adClean = ad10f.copy() fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10,4), gridspec_kw={'wspace':0.25}, constrained_layout=True) ax1_dict = sc.pl.scatter(adClean, x='total_counts', y='pct_counts_mt', ax=ax1, show=False) ax2_dict = sc.pl.scatter(adClean, x='total_counts', y='n_genes_by_counts',ax=ax2, show=False) ax3_dict = sc.pl.scatter(adClean, x='pct_counts_ribo', y='n_genes_by_counts',ax=ax3, show=False) plt.show()

Now, do the filtering.

First, keep cells with fewer than 20% mitochondrially encoded gene total UMIs

In [40]:

adClean = adClean[adClean.obs['pct_counts_mt']<20,:].copy()
adClean.n_obs

adClean = adClean[adClean.obs['pct_counts_mt']<20,:].copy() adClean.n_obs

Out[40]:

11047

• Second, filter based on total number of genes detected (at least 500).
• Third, filter based on total number of counts (fewer than than 30,000)
• Fourth, keep genes that are detected in at least 10 cells.

In [43]:

adClean.var_names_make_unique()
sc.pp.filter_cells(adClean, min_genes=500)
print(adClean.n_obs)

sc.pp.filter_cells(adClean, max_counts=30000)
print(adClean.n_obs)

sc.pp.filter_genes(adClean, min_cells=10)
print(adClean.shape)

adClean.var_names_make_unique() sc.pp.filter_cells(adClean, min_genes=500) print(adClean.n_obs) sc.pp.filter_cells(adClean, max_counts=30000) print(adClean.n_obs) sc.pp.filter_genes(adClean, min_cells=10) print(adClean.shape)

10924
10891
(10891, 17422)

Normalization¶

Simple library size scaling, then log transform. There are many other ways to correct for cell-to-cell technical variation, for example different cell lysis efficiencies, but this method is good enough for this data set and our goals.

In [44]:

adNorm = adClean.copy()
sc.pp.normalize_total(adNorm , target_sum=1e4)
sc.pp.log1p(adNorm)
adNorm.layers['lognorm'] = adNorm.X.copy()

adNorm = adClean.copy() sc.pp.normalize_total(adNorm , target_sum=1e4) sc.pp.log1p(adNorm) adNorm.layers['lognorm'] = adNorm.X.copy()

Highly variable genes (HVG)¶

It is common practice to limit some parts of analysis to those variables/genes that exhibit some degree of variation in their values across the data. We call these highly variable genes, or HVG for short. To find these, calculate some gene statistics, which when combined with thresholds below, determine which genes are considered HVG. The relevant metrics are:

1. mean expression
2. dispersion, which is the variance / mean.

Variance is defined as the expected squared deviation of gene expression. The normalized dispersion is calculated by scaling based on a bin of mean expression.

In [45]:

n_hvg = 2000
sc.pp.highly_variable_genes(adNorm, n_top_genes=n_hvg, flavor='cell_ranger')
# sc.pp.highly_variable_genes(adNorm , min_mean=0.0125, max_mean=6, min_disp=0.25)
adNorm.var

n_hvg = 2000 sc.pp.highly_variable_genes(adNorm, n_top_genes=n_hvg, flavor='cell_ranger') # sc.pp.highly_variable_genes(adNorm , min_mean=0.0125, max_mean=6, min_disp=0.25) adNorm.var

Out[45]:

	gene_ids	feature_types	genome	mt	ribo	n_cells_by_counts	mean_counts	pct_dropout_by_counts	total_counts	n_cells	highly_variable	means	dispersions	dispersions_norm
AL627309.1	ENSG00000238009	Gene Expression	GRCh38	False	False	60	0.005183	99.490186	61.0	60	False	0.004502	0.917161	-0.060098
AL669831.5	ENSG00000237491	Gene Expression	GRCh38	False	False	679	0.062367	94.230606	734.0	665	False	0.048407	0.856350	-0.258551
FAM87B	ENSG00000177757	Gene Expression	GRCh38	False	False	13	0.001190	99.889540	14.0	13	False	0.000770	0.719899	-1.331091
LINC00115	ENSG00000225880	Gene Expression	GRCh38	False	False	350	0.031269	97.026085	368.0	337	False	0.024243	0.848471	-0.828740
FAM41C	ENSG00000230368	Gene Expression	GRCh38	False	False	313	0.027190	97.340471	320.0	308	False	0.022719	0.883760	-0.320399
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AC011043.1	ENSG00000276256	Gene Expression	GRCh38	False	False	77	0.006882	99.345739	81.0	74	False	0.005875	0.988261	0.642865
AL592183.1	ENSG00000273748	Gene Expression	GRCh38	False	False	32	0.002719	99.728099	32.0	30	False	0.002267	0.978485	0.592432
AC007325.4	ENSG00000278817	Gene Expression	GRCh38	False	False	239	0.020902	97.969241	246.0	233	False	0.015576	0.787513	-1.681796
AL354822.1	ENSG00000278384	Gene Expression	GRCh38	False	False	319	0.028210	97.289489	332.0	303	False	0.023072	0.906420	0.006022
AC240274.1	ENSG00000271254	Gene Expression	GRCh38	False	False	101	0.008752	99.141813	103.0	99	False	0.006624	0.794754	-1.529179

17422 rows × 14 columns

In [46]:

sc.pl.highly_variable_genes(adNorm)

sc.pl.highly_variable_genes(adNorm)

Dimensionality reduction with Principal components analysis (PCA)¶

Why performan dimentaionality reduction?

1. Genes are expressed in coordinated fashion, meaning that many have correlated expression patterns and so PCA reduces the computational complexity of downstream analysis
2. scRNA-seq data is noisy on a per gene basis.

PCA allows us to reduce a high dimensional data set into a lower dimension in which much of the total variation is maintained. To understand PCA, you need to know linear algebra. In essence, it identifies sets of linear combinations of genes in such a way that a PC is uncorrelated with other PCs and that explains most variation in the data. The function call sc.tl.pca does the following:

1. computes the covariance matrix (correlation between each pair of genes)
2. finds eigenvectors (directions of axes that maximize that variance), orthogonal to each other

Later, we select a threshold (n_pcs) as the number of PCs that contribute most to the total variation on our data. We use these n_pcs PCs to compute cell-to-cell distances for embedding (2D visualization via UMAP) and for clustering of cells.

In [47]:

adNorm

adNorm

Out[47]:

AnnData object with n_obs × n_vars = 10891 × 17422
    obs: 'sample_name', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'n_counts'
    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'log1p', 'hvg'
    layers: 'counts', 'lognorm'

In [48]:

sc.tl.pca(adNorm , mask_var='highly_variable')
adNorm

sc.tl.pca(adNorm , mask_var='highly_variable') adNorm

Out[48]:

AnnData object with n_obs × n_vars = 10891 × 17422
    obs: 'sample_name', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'n_counts'
    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'log1p', 'hvg', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
    layers: 'counts', 'lognorm'

In [49]:

sc.pl.pca_loadings(adNorm, components = [1,2,3,4], n_points=6, include_lowest=False)

sc.pl.pca_loadings(adNorm, components = [1,2,3,4], n_points=6, include_lowest=False)

In [50]:

sc.pl.pca_variance_ratio(adNorm, 50)

sc.pl.pca_variance_ratio(adNorm, 50)

Marker genes¶

Here PBMC cell types and some genes that have been used to identify them:
Monocytes: CD14, CD68, LYZ
Dendritic: CLEC4C, FLT3
NK cells: GNLY, NCAM1
B cells: CD19, CD79A, CD79B
T cells: CD3D, CD3E, TRAC, TRBC

In [51]:

markerGenes = ['CD14','CLEC4C', 'CD19',  'GNLY', 'CD3D']
sc.pl.pca(adNorm , color=markerGenes, ncols=2, layer='counts')

markerGenes = ['CD14','CLEC4C', 'CD19', 'GNLY', 'CD3D'] sc.pl.pca(adNorm , color=markerGenes, ncols=2, layer='counts')

In [52]:

sc.pl.pca(adNorm , color=markerGenes, ncols=2)

sc.pl.pca(adNorm , color=markerGenes, ncols=2)

In [53]:

sc.pl.pca(adNorm, color=['total_counts', 'pct_counts_mt', 'pct_counts_ribo'], s=25,ncols=3)

sc.pl.pca(adNorm, color=['total_counts', 'pct_counts_mt', 'pct_counts_ribo'], s=25,ncols=3)

kNN cell-cell distances¶

The k-nearest neighbor graph generates an adjacency matrix by finding, for each cell, the k cells that are closest to it. This helps to reduce noise in computing cell-cell distances or similarities and is important for embedding the cells in a 2D space, and for community detection algorithms. Two arguments are n_neighbors, or k, and the number of PCs to use when computing cell-cell distances.

In [54]:

n_neighbors = 20
n_pcs = 10
sc.pp.neighbors(adNorm, n_neighbors=n_neighbors, n_pcs=n_pcs)
adNorm

n_neighbors = 20 n_pcs = 10 sc.pp.neighbors(adNorm, n_neighbors=n_neighbors, n_pcs=n_pcs) adNorm

Out[54]:

AnnData object with n_obs × n_vars = 10891 × 17422
    obs: 'sample_name', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'n_counts'
    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'log1p', 'hvg', 'pca', 'neighbors'
    obsm: 'X_pca'
    varm: 'PCs'
    layers: 'counts', 'lognorm'
    obsp: 'distances', 'connectivities'

Visualization of cell-cell similarities¶

UMAP embedding¶

This an elaboration on the t-SNE embedding approach. Both of these methods try to project cells into a reduced (typically 2) dimensional coordinate system while maintaining both the global and local structure of the high dimensional space of the data (i.e. cells that are distant from each other high dimensions should still be distant in the reduced embedding; same is true for cells that are near each other). The distances come from the kNN graph.

In [55]:

sc.tl.umap(adNorm)

sc.tl.umap(adNorm)

In [56]:

sc.pl.umap(adNorm, color=markerGenes, alpha=.75, s=15, ncols=2)

sc.pl.umap(adNorm, color=markerGenes, alpha=.75, s=15, ncols=2)

There are lots of ways to customize our plots. See the Scanpy visualization documentation for details. Here, lets change the colors and remove the boxes and axes labels.

In [57]:

sc.pl.umap(adNorm, color= genes_monocyte, alpha=.75, s=12, frameon=False, ncols=2, legend_fontoutline=2,legend_fontsize=14, cmap="Reds")

sc.pl.umap(adNorm, color= genes_monocyte, alpha=.75, s=12, frameon=False, ncols=2, legend_fontoutline=2,legend_fontsize=14, cmap="Reds")

Unsupervised clustering¶

Let's try to assign cells to distinct groups or clusters based on the cell-to-cell distances. Leiden and Louvain are examples of community detection methods that perform this task by searching for the group assignment that maximize within group similarity (which is equivalent to within group edges of a knn graph) and to minimize between group similarity.

In [66]:

clustering_name = "leiden_cluster"
sc.tl.leiden(adNorm, 0.1, flavor="igraph", n_iterations=2,key_added=clustering_name)

clustering_name = "leiden_cluster" sc.tl.leiden(adNorm, 0.1, flavor="igraph", n_iterations=2,key_added=clustering_name)

In [67]:

sc.pl.umap(adNorm, color=[clustering_name], alpha=.75, s=9, legend_loc='on data', frameon=False, ncols=1, legend_fontoutline=2,legend_fontsize=11, cmap="Reds", use_raw=False)

sc.pl.umap(adNorm, color=[clustering_name], alpha=.75, s=9, legend_loc='on data', frameon=False, ncols=1, legend_fontoutline=2,legend_fontsize=11, cmap="Reds", use_raw=False)

Make a figure that shows the embedding and expression of marker genes side-by-side. Note that I am adding markers for T cell, B cell, and Monocyte sub-types

In [71]:

marker_genes_broad_dict = {
    'B cell': ['CD79A', 'PAX5'],
    'Dendritic': ['CLEC4C', 'FCER1A'],
    'Monocyte': ['CSF1R', 'FCGR3A'],
    'NK cell': ['NKG7', 'GNLY'],
    'T cell': ['TRAC', 'CD3E'],
}
sc.tl.dendrogram(adNorm, groupby=clustering_name)


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25})
ax1_dict = sc.pl.umap(adNorm,color=[clustering_name], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False)
ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, groupby=clustering_name, dendrogram=True,ax=ax2, show=False)
plt.show()

marker_genes_broad_dict = { 'B cell': ['CD79A', 'PAX5'], 'Dendritic': ['CLEC4C', 'FCER1A'], 'Monocyte': ['CSF1R', 'FCGR3A'], 'NK cell': ['NKG7', 'GNLY'], 'T cell': ['TRAC', 'CD3E'], } sc.tl.dendrogram(adNorm, groupby=clustering_name) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25}) ax1_dict = sc.pl.umap(adNorm,color=[clustering_name], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False) ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, groupby=clustering_name, dendrogram=True,ax=ax2, show=False) plt.show()

WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2, etc.
var_group_labels: B cell, Dendritic, Monocyte, etc.

STOP HERE FOR NOW¶

Cluster annotation¶

We can walk down the rows of the dotplot and guess at the cell types of some of these clusters based on the specificty of marker expression:

cluster	markers of	cell type
5	B, Dendritic, Mono	???
1	Monocyte	Monocyte
4	Monocyte, T	???
3	B	B-cell
6	Dendritic (B/Mono?)	Dendritic cell
7	none	Mystery cell
0	T cell	T cell
2	NK/T	???

Some clusters are ambiguous because they express markers of more than one cell type. This could be due to the cluster containing > 1 cell type, or because the cell barcodes are doublets. If the former, then we should be able to sub cluster them. Let's try this.

Clusters to refine are 5, 4, and 2.

In [48]:

sc.tl.leiden(adNorm,.1, restrict_to=["leiden",["5"]])
sc.tl.dendrogram(adNorm, "leiden_R")
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25})
ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False)
ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False)
plt.show()

sc.tl.leiden(adNorm,.1, restrict_to=["leiden",["5"]]) sc.tl.dendrogram(adNorm, "leiden_R") fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25}) ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False) ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False) plt.show()

WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2, etc.
var_group_labels: B cell, Dendritic, Monocyte, etc.

Cluster 5 is split into 2 clusters now. Both of these sub-clusters express the Monocyte marker CD14, but are distinguished by their expression of dendritic marker FCER1A and the B cell genes CD79A and PAX5. Let's look at the QC metrics to see if that can give us additional hints as to whether these are real cells or are doublets.

In [49]:

sc.pl.violin(adNorm, ['total_counts'], groupby='leiden_R' )

sc.pl.violin(adNorm, ['total_counts'], groupby='leiden_R' )

Cluster 5 (and each sub-cluster) has a high number of Total counts. So let's annotate these as 'doublet'.

What about the other questionable clusters 4 and 2? Can we refine those?

In [50]:

# cluster 2 first
sc.tl.leiden(adNorm,.05, restrict_to=["leiden",["2"]])
sc.tl.dendrogram(adNorm, "leiden_R")
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25})
ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False)
ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False)
plt.show()

# cluster 2 first sc.tl.leiden(adNorm,.05, restrict_to=["leiden",["2"]]) sc.tl.dendrogram(adNorm, "leiden_R") fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25}) ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False) ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False) plt.show()

WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2,0, etc.
var_group_labels: B cell, Dendritic, Monocyte, etc.

It looks like 2,0 is T cell, and 2,1 is Natural Killer cell. Note that 2,0 still has a distinct profile as compared to 0.

What about cluster 4? And this time, when we do the sub-clustering, let's keep the way that we split up cluster 2.

In [51]:

sc.tl.leiden(adNorm,.1, restrict_to=["leiden_R",["4"]])
sc.tl.dendrogram(adNorm, "leiden_R")
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25})
ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False)
ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False)
plt.show()

sc.tl.leiden(adNorm,.1, restrict_to=["leiden_R",["4"]]) sc.tl.dendrogram(adNorm, "leiden_R") fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25}) ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False) ax2_dict = sc.pl.dotplot(adNorm, marker_genes_broad_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False) plt.show()

WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2,0, etc.
var_group_labels: B cell, Dendritic, Monocyte, etc.

Is 4,1 multiplet?

In [52]:

sc.pl.violin(adNorm, ['total_counts', 'n_genes_by_counts'], groupby='leiden_R' )

sc.pl.violin(adNorm, ['total_counts', 'n_genes_by_counts'], groupby='leiden_R' )

Yes, it looks like 4,1 is similar in these QC profiles to cluster 5.

So our updated annotation table is now:

cluster	markers of	cell type
5	B, Dendritic, Mono	doublet
1	Monocyte	Monocyte
4,0	Monocyte	Monocyte
3	B	B-cell
6	Dendritic (B/Mono?)	Dendritic cell
7	none	Mystery cell
0	T cell	T cell
2,0	T cell	T cell
2,1	NK cell	NK cell

Why do we have two Monocyte clusters and two T cells clusters?

In [53]:

marker_genes_sub_dict = {
    'B cell': ['CD79A'],
    'Dendritic': ['CLEC4C'],
    'CD14 monocyte': ['CD14'],
    'CD16 monocyte': ['FCGR3A'],
    'NK cell': ['GNLY'],
    'CD4 T cell': ['CD4', 'GATA3'],
    'CD8 T cell': ['CD8A', 'EOMES']
}

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25})
ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False)
ax2_dict = sc.pl.dotplot(adNorm, marker_genes_sub_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False)
plt.show()

marker_genes_sub_dict = { 'B cell': ['CD79A'], 'Dendritic': ['CLEC4C'], 'CD14 monocyte': ['CD14'], 'CD16 monocyte': ['FCGR3A'], 'NK cell': ['GNLY'], 'CD4 T cell': ['CD4', 'GATA3'], 'CD8 T cell': ['CD8A', 'EOMES'] } fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4), gridspec_kw={'wspace':0.25}) ax1_dict = sc.pl.umap(adNorm,color=['leiden_R'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False) ax2_dict = sc.pl.dotplot(adNorm, marker_genes_sub_dict, 'leiden_R', dendrogram=True,ax=ax2, show=False) plt.show()

WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2,0, etc.
var_group_labels: B cell, Dendritic, CD14 monocyte, etc.

We are close to a final annotation now:

cluster	markers of	cell type
5	B, Dendritic, Mono	doublet
1	Monocyte	CD14 Monocyte
4,0	Monocyte	CD16 Monocyte
3	B	B-cell
6	Dendritic (B/Mono?)	Dendritic cell
7	none	Mystery cell
0	T cell	CD4 T cell
2,0	T cell	CD8 T cell
2,1	NK cell	NK cell

But what is cluster 7? It has low counts. If it is just ambient 'soup', then we would not expect it to have high levels of cell type specific genes. Let's do differential gene expression analysis to see what is upregulated in this cluster.

Differential gene expression¶

In [54]:

sc.tl.rank_genes_groups(adNorm, use_raw=False, groupby="leiden_R")

sc.tl.rank_genes_groups(adNorm, use_raw=False, groupby="leiden_R")

In [55]:

sc.pl.rank_genes_groups_dotplot(adNorm, n_genes=5, groupby="leiden_R", dendrogram=True, key='rank_genes_groups')

sc.pl.rank_genes_groups_dotplot(adNorm, n_genes=5, groupby="leiden_R", dendrogram=True, key='rank_genes_groups')

In [56]:

sc.pl.rank_genes_groups_dotplot(adNorm, n_genes=20, groupby="leiden_R", dendrogram=True, key='rank_genes_groups', groups=['7'])

sc.pl.rank_genes_groups_dotplot(adNorm, n_genes=20, groupby="leiden_R", dendrogram=True, key='rank_genes_groups', groups=['7'])

WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: 0, 1, 2,0, etc.
var_group_labels: 7

Although this result could be refined and explored a lot, it is sufficient for us to address our question about the identity of cluster 7 as follows:

Help from ChatGPT to annotate cells

Now, let's add the annotation to the anndata.obs and discard the other cells

cluster	markers of	cell type
1	Monocyte	CD14 Monocyte
4,0	Monocyte	CD16 Monocyte
3	B	B-cell
6	Dendritic (B/Mono?)	Dendritic cell
7	none	Platelet
0	T cell	CD4 T cell
2,0	T cell	CD8 T cell
2,1	NK cell	NK cell

In [57]:

tokeep = ["1", "4,0", "3", "6", "7", "0", "2,0", "2,1"]
adNorm2 = adNorm.copy()
adNorm2 = adNorm2[adNorm2.obs['leiden_R'].isin(tokeep)].copy()
adNorm2.shape
adNorm2.obs['leiden_R'].value_counts()

tokeep = ["1", "4,0", "3", "6", "7", "0", "2,0", "2,1"] adNorm2 = adNorm.copy() adNorm2 = adNorm2[adNorm2.obs['leiden_R'].isin(tokeep)].copy() adNorm2.shape adNorm2.obs['leiden_R'].value_counts()

Out[57]:

leiden_R
0      3563
1      3115
3      1604
2,0    1031
2,1     611
4,0     368
6        81
7        60
Name: count, dtype: int64

In [58]:

cell_dict = {'Dendritic': ['6'],
             'CD14 Monocyte': ['1'],
             'CD16 Monocyte': ["4,0"],
             'B cell': ['3'],
             'Platelet': ['7'],
             'CD4 T cell': ['0'],
             'CD8 T cell': ["2,0"],
             'NK cell': ["2,1"]
}

marker_genes_dict = {
    'B cell': ['CD79A'],
    'Dendritic': ['CLEC4C'],
    'CD14 monocyte': ['CD14'],
    'CD16 monocyte': ['FCGR3A'],
    'NK cell': ['GNLY'],
    'CD4 T cell': ['CD4', 'GATA3'],
    'CD8 T cell': ['CD8A', 'EOMES'],
    'Platelet': ['PF4', 'CAVIN2']
}


new_obs_name = 'cell_type'
adNorm2.obs[new_obs_name] = np.nan

for i in cell_dict.keys():
    ind = pd.Series(adNorm2.obs.leiden_R).isin(cell_dict[i])
    adNorm2.obs.loc[ind,new_obs_name] = i

adNorm2.obs['cell_type'] = adNorm2.obs['cell_type'].astype("category")

cell_dict = {'Dendritic': ['6'], 'CD14 Monocyte': ['1'], 'CD16 Monocyte': ["4,0"], 'B cell': ['3'], 'Platelet': ['7'], 'CD4 T cell': ['0'], 'CD8 T cell': ["2,0"], 'NK cell': ["2,1"] } marker_genes_dict = { 'B cell': ['CD79A'], 'Dendritic': ['CLEC4C'], 'CD14 monocyte': ['CD14'], 'CD16 monocyte': ['FCGR3A'], 'NK cell': ['GNLY'], 'CD4 T cell': ['CD4', 'GATA3'], 'CD8 T cell': ['CD8A', 'EOMES'], 'Platelet': ['PF4', 'CAVIN2'] } new_obs_name = 'cell_type' adNorm2.obs[new_obs_name] = np.nan for i in cell_dict.keys(): ind = pd.Series(adNorm2.obs.leiden_R).isin(cell_dict[i]) adNorm2.obs.loc[ind,new_obs_name] = i adNorm2.obs['cell_type'] = adNorm2.obs['cell_type'].astype("category")

/var/folders/1_/v5grqr5n7kj42276bb6ryxj00000gn/T/ipykernel_66578/4042500018.py:28: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'Dendritic' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  adNorm2.obs.loc[ind,new_obs_name] = i

In [59]:

sc.tl.dendrogram(adNorm2, "cell_type")
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5), gridspec_kw={'wspace':0.4})
ax1_dict = sc.pl.umap(adNorm2,color=['cell_type'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False)
ax2_dict = sc.pl.dotplot(adNorm2, marker_genes_dict, 'cell_type', dendrogram=True,ax=ax2, show=False)
plt.show()

sc.tl.dendrogram(adNorm2, "cell_type") fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5), gridspec_kw={'wspace':0.4}) ax1_dict = sc.pl.umap(adNorm2,color=['cell_type'], alpha=.75, s=10, legend_loc='on data', ax=ax1, show=False) ax2_dict = sc.pl.dotplot(adNorm2, marker_genes_dict, 'cell_type', dendrogram=True,ax=ax2, show=False) plt.show()

WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: B cell, CD14 Monocyte, CD16 Monocyte, etc.
var_group_labels: B cell, Dendritic, CD14 monocyte, etc.

In [60]:

sc.pl.pca(adNorm2, color=['cell_type', 'GNLY'], alpha=.75, s=15, projection='3d')

sc.pl.pca(adNorm2, color=['cell_type', 'GNLY'], alpha=.75, s=15, projection='3d')

Differential expression analysis¶

Let's identify genes that are preferenntially expressed in each cluster versus all the others as a final step to annotate these clusters and to remove ones that are likely to be doublets.

In [61]:

sc.tl.rank_genes_groups(adNorm2, use_raw=False, groupby="cell_type")

sc.tl.rank_genes_groups(adNorm2, use_raw=False, groupby="cell_type")

Dot plot of differentially expressed genes¶

First, we will apply a filter so that we only display genes that meet additional criteria beyond statistical: fold change, % expressed in cluster, % expressed in other cells

In [62]:

sc.tl.filter_rank_genes_groups(adNorm2, min_fold_change=.7, min_in_group_fraction=.5, max_out_group_fraction=.15)
sc.pl.rank_genes_groups_dotplot(adNorm2, n_genes=6, groupby="cell_type", dendrogram=True, key='rank_genes_groups_filtered')

sc.tl.filter_rank_genes_groups(adNorm2, min_fold_change=.7, min_in_group_fraction=.5, max_out_group_fraction=.15) sc.pl.rank_genes_groups_dotplot(adNorm2, n_genes=6, groupby="cell_type", dendrogram=True, key='rank_genes_groups_filtered')

Resources¶

Tools¶

• Colab
  • https://colab.research.google.com/
• Jupyter
  • https://jupyter.org/
• CellRanger
  • https://www.10xgenomics.com/support/software/cell-ranger/latest/advanced/cr-barcode-rank-plot
  • https://www.10xgenomics.com/support/software/cell-ranger/latest/algorithms-overview/cr-gex-algorithm

Literature¶

• Best practices
  • best practices in sc analysis online book
  • paper associated with the above online book
• Scanpy
  • https://scanpy.readthedocs.io/en/stable/
  • https://scanpy.readthedocs.io/en/stable/tutorials.html
  • https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html
  • https://scanpy-tutorials.readthedocs.io/en/latest/plotting/advanced.html
  • https://scanpy-tutorials.readthedocs.io/en/latest/plotting/core.html
• AnnData
  • https://anndata.readthedocs.io/en/latest/tutorials/notebooks/getting-started.html
  • https://anndata.readthedocs.io/en/latest/index.html
• QC
  • https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02136-7